Literature DB >> 29944078

Current Applications and Future Impact of Machine Learning in Radiology.

Garry Choy1, Omid Khalilzadeh1, Mark Michalski1, Synho Do1, Anthony E Samir1, Oleg S Pianykh1, J Raymond Geis1, Pari V Pandharipande1, James A Brink1, Keith J Dreyer1.   

Abstract

Recent advances and future perspectives of machine learning techniques offer promising applications in medical imaging. Machine learning has the potential to improve different steps of the radiology workflow including order scheduling and triage, clinical decision support systems, detection and interpretation of findings, postprocessing and dose estimation, examination quality control, and radiology reporting. In this article, the authors review examples of current applications of machine learning and artificial intelligence techniques in diagnostic radiology. In addition, the future impact and natural extension of these techniques in radiology practice are discussed. © RSNA, 2018.

Mesh:

Year:  2018        PMID: 29944078      PMCID: PMC6542626          DOI: 10.1148/radiol.2018171820

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  79 in total

1.  3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients.

Authors:  Dong Nie; Han Zhang; Ehsan Adeli; Luyan Liu; Dinggang Shen
Journal:  Med Image Comput Comput Assist Interv       Date:  2016-10-02

2.  Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma.

Authors:  Bin Zhang; Xin He; Fusheng Ouyang; Dongsheng Gu; Yuhao Dong; Lu Zhang; Xiaokai Mo; Wenhui Huang; Jie Tian; Shuixing Zhang
Journal:  Cancer Lett       Date:  2017-06-10       Impact factor: 8.679

Review 3.  Segmentation of joint and musculoskeletal tissue in the study of arthritis.

Authors:  Valentina Pedoia; Sharmila Majumdar; Thomas M Link
Journal:  MAGMA       Date:  2016-02-25       Impact factor: 2.310

Review 4.  Computer-aided diagnosis: A survey with bibliometric analysis.

Authors:  Ryohei Takahashi; Yuya Kajikawa
Journal:  Int J Med Inform       Date:  2017-02-17       Impact factor: 4.046

5.  Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring.

Authors:  Michiel Kallenberg; Kersten Petersen; Mads Nielsen; Andrew Y Ng; Christian Igel; Celine M Vachon; Katharina Holland; Rikke Rass Winkel; Nico Karssemeijer; Martin Lillholm
Journal:  IEEE Trans Med Imaging       Date:  2016-02-18       Impact factor: 10.048

Review 6.  Medical Malpractice: Reform for Today's Patients and Clinicians.

Authors:  Jason A Stamm; Karen A Korzick; Kristen Beech; Kenneth E Wood
Journal:  Am J Med       Date:  2015-09-25       Impact factor: 4.965

Review 7.  A review of segmentation and deformable registration methods applied to adaptive cervical cancer radiation therapy treatment planning.

Authors:  Soumya Ghose; Lois Holloway; Karen Lim; Philip Chan; Jacqueline Veera; Shalini K Vinod; Gary Liney; Peter B Greer; Jason Dowling
Journal:  Artif Intell Med       Date:  2015-05-16       Impact factor: 5.326

8.  Automated diagnosis of prostate cancer in multi-parametric MRI based on multimodal convolutional neural networks.

Authors:  Minh Hung Le; Jingyu Chen; Liang Wang; Zhiwei Wang; Wenyu Liu; Kwang-Ting Tim Cheng; Xin Yang
Journal:  Phys Med Biol       Date:  2017-07-24       Impact factor: 3.609

9.  Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework.

Authors:  Luke Oakden-Rayner; Gustavo Carneiro; Taryn Bessen; Jacinto C Nascimento; Andrew P Bradley; Lyle J Palmer
Journal:  Sci Rep       Date:  2017-05-10       Impact factor: 4.379

10.  Automatic detection of abnormalities in mammograms.

Authors:  Zobia Suhail; Mansoor Sarwar; Kashif Murtaza
Journal:  BMC Med Imaging       Date:  2015-11-06       Impact factor: 1.930

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  139 in total

1.  Use of radiomics based on 18F-FDG PET/CT and machine learning methods to aid clinical decision-making in the classification of solitary pulmonary lesions: an innovative approach.

Authors:  Yi Zhou; Xue-Lei Ma; Ting Zhang; Jian Wang; Tao Zhang; Rong Tian
Journal:  Eur J Nucl Med Mol Imaging       Date:  2021-02-05       Impact factor: 9.236

Review 2.  Neuroimaging in Psychiatry and Neurodevelopment: why the emperor has no clothes.

Authors:  Ashley N Anderson; Jace B King; Jeffrey S Anderson
Journal:  Br J Radiol       Date:  2019-03-15       Impact factor: 3.039

Review 3.  Current applications and future directions of deep learning in musculoskeletal radiology.

Authors:  Pauley Chea; Jacob C Mandell
Journal:  Skeletal Radiol       Date:  2019-08-04       Impact factor: 2.199

Review 4.  Artificial intelligence for precision education in radiology.

Authors:  Michael Tran Duong; Andreas M Rauschecker; Jeffrey D Rudie; Po-Hao Chen; Tessa S Cook; R Nick Bryan; Suyash Mohan
Journal:  Br J Radiol       Date:  2019-07-26       Impact factor: 3.039

5.  Artificial intelligence in musculoskeletal oncological radiology.

Authors:  Matjaz Vogrin; Teodor Trojner; Robi Kelc
Journal:  Radiol Oncol       Date:  2020-11-10       Impact factor: 2.991

6.  Will Artificial Intelligence Replace Radiologists?

Authors:  Curtis P Langlotz
Journal:  Radiol Artif Intell       Date:  2019-05-15

7.  Preparing Radiologists to Lead in the Era of Artificial Intelligence: Designing and Implementing a Focused Data Science Pathway for Senior Radiology Residents.

Authors:  Walter F Wiggins; M Travis Caton; Kirti Magudia; Sha-Har A Glomski; Elizabeth George; Michael H Rosenthal; Glenn C Gaviola; Katherine P Andriole
Journal:  Radiol Artif Intell       Date:  2020-11-04

8.  Automated CT biomarkers for opportunistic prediction of future cardiovascular events and mortality in an asymptomatic screening population: a retrospective cohort study.

Authors:  Perry J Pickhardt; Peter M Graffy; Ryan Zea; Scott J Lee; Jiamin Liu; Veit Sandfort; Ronald M Summers
Journal:  Lancet Digit Health       Date:  2020-03-02

9.  An Open-Source, Vender Agnostic Hardware and Software Pipeline for Integration of Artificial Intelligence in Radiology Workflow.

Authors:  Jae Ho Sohn; Yeshwant Reddy Chillakuru; Stanley Lee; Amie Y Lee; Tatiana Kelil; Christopher Paul Hess; Youngho Seo; Thienkhai Vu; Bonnie N Joe
Journal:  J Digit Imaging       Date:  2020-08       Impact factor: 4.056

Review 10.  Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives.

Authors:  Krzysztof J Geras; Ritse M Mann; Linda Moy
Journal:  Radiology       Date:  2019-09-24       Impact factor: 11.105

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